Carbon reduction multidimensional ternary model for robotic 3D printing prefabricated construction across hotels star levels
ホテルの星級別ロボット3Dプリンティングによるプレハブ建設の炭素削減多次元三元モデル (AI 翻訳)
Gangwei Cai, Xiaoting Guo, Jian Chen, Qian Wu, Yiru Pan, Yifan Yao, 叶璐 YE Lu, Yuguang Sun, Feidong Lu, Weijun Gao
🤖 gxceed AI 要約
日本語
本研究は、中国杭州の低星級ホテルを対象に、ロボット3Dプリンティングとプレハブ工法による炭素排出削減効果を2018年から2023年にわたり評価した。多次元三元時空間モデルを用いて、従来工法と比較し、2星・3星ホテルで約20-35%の削減を確認した。ホテルの等級や地域分布によって削減効果が異なることを示し、低星級ホテルでの標準化モジュール工法の有効性を明らかにした。
English
This study evaluates carbon emission reduction potential of robotic 3D printing and prefabricated construction in lower-star hotels in Hangzhou, China (2018-2023). Using a multidimensional ternary spatiotemporal model, it finds 20-35% embodied carbon reductions in 2- and 3-star hotels compared to conventional methods. Reduction effectiveness varies by hotel class and spatial distribution, with standardized modular construction benefiting lower-star hotels more. The framework supports targeted low-carbon construction strategies and carbon-economic decoupling in urban environments.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の建設業界では、カーボンニュートラル達成に向けて建設プロセスの脱炭素化が急務であり、本研究成果はプレハブ工法や3Dプリンティングの炭素削減効果を定量的に示すことで、日本の建設会社や不動産事業者にとってベンチマークとなる。特に、SSBJ開示やサプライチェーン排出量算定において、建設資材の製造段階（Scope3）の削減に寄与する知見を提供する。
In the global GX context
This paper contributes to global construction decarbonization literature by quantifying embodied carbon reductions from prefabrication and 3D printing in the hospitality sector. It offers a scalable framework for carbon accounting that can inform ISSB-aligned disclosure and transition finance decisions in real estate and construction. The spatiotemporal analysis provides evidence for region-specific low-carbon policies, relevant to global urban sustainability efforts.
👥 読者別の含意
🔬研究者:Provides a quantitative framework for evaluating embodied carbon in construction, useful for carbon accounting methodology development.
🏢実務担当者:Construction and hospitality firms can use the findings to justify investments in prefabrication and 3D printing for carbon reduction.
🏛政策担当者:Offers evidence for targeted low-carbon construction policies and carbon-economic decoupling strategies in urban areas.
📄 Abstract（原文）
This study evaluates the carbon emission reduction potential of industrialized construction technologies-specifically robotic 3D printing and prefabrication-in lower-star hotels in Hangzhou, China, from 2018 to 2023. A multidimensional ternary spatiotemporal model is developed to quantify the interactions among temporal, spatial, and hotel classification dimensions, enabling a systematic comparison between conventional construction (CC) and prefabricated construction (PC). The results show that prefabricated construction consistently reduces embodied carbon emissions across hotel categories, with the most significant reduction observed in 2-star and 3-star hotels, where carbon emissions can be reduced by approximately 20-35% compared to conventional methods. The findings further indicate that carbon reduction effectiveness varies by hotel classification and spatial distribution, with lower-star hotels benefiting more from standardized modular construction, while high-star hotels exhibit relatively lower reduction efficiency due to structural complexity. Spatiotemporal analysis also reveals heterogeneous decoupling patterns across districts, suggesting that carbon-economic relationships are strongly influenced by regional development conditions and construction intensity. This study advances the integration of carbon accounting and construction technology assessment by providing a quantitative and scalable framework for evaluating embodied carbon reduction in the hospitality sector. The findings offer robust evidence to support targeted low-carbon construction strategies and contribute to carbon-economic decoupling pathways in resource-constrained urban environments, aligning with the scope of carbon balance and management.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1186/s13021-026-00447-zfirst seen 2026-05-24 04:58:06 · last seen 2026-06-17 07:06:04
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